Scalability and Load-Balancing Problems in Parallel Reservoir Simulation

Improvement of tasks scheduling algorithm based on load balancing candidate method under cloud computing environment

Dynamic load balancing techniques prevents computer nodes from overloading unevenly while leaving other idle. It is considered as one of the most challenging topics in parallel computing. Moreover, it is essential for increasing the efficiency of highly parallel systems especially in solving multitask problems with unpredictable load estimates. Particularly, over each processor in the parallel systems and interconnection networks. This paper focuses on developing an efficient algorithm for load balancing on Hyper Hexa Cell (HHC) interconnection network, namely, HHCLB algorithm. Basically, the Dimension Exchange Method (DEM) approach is used in this paper to construct a new load balancing approach on the network of HHC interconnections. Thus, an algorithm was introduced and simulated using java threads, where the performance of the algorithm is evaluated both analytically and experimentally. The evaluation was in terms of various performance metrics, including, execution time, load balancing accuracy, communication cost. By implementing the proposed load balancing algorithm to the HHC network, a high degree of accuracy and minimal execution time was achieved. It is important to highlight that the algorithm recorded small gap between the execution time for small number of processors and large number of processors. For instance, the algorithm achieved 0.14 seconds for balancing the load of 6 processors while 0.59 seconds for balancing the load of 3072 processors. This proves how effective the algorithm is in balancing the load for different network sizes from small to large number of processors, with a slight difference in execution time.

For better utilization of computing resources, it is important to consider parallel programming environments in which the number of available processors varies at runtime. In this paper, we discuss runtime support for data parallel programming in such an adaptive environment. Executing data parallel-programs in an adaptive environment requires redistributing data when the number of processors changes, and also requires determining new loop bounds and communication patterns for the new set of processors. We have developed a runtime library to provide this support. We also present performance results for a multiblock Navier-Stokes solver run on a network of workstations using PVM for message passing. Our experiments show that if the number of processors is not varied frequently, the cost of data redistribution is not significant compared to the time required for the actual computations. >

The traditional networks are facing difficulties in managing the services offered by cloud computing, big data, and the Internet of Things as the users have become more dependent on their services. Software-Defined Networking (SDN) has pulled enthusiasm in the integration process of technologies and function as per the user's requirements for both academia and industry, and it has begun to be embraced in actual framework usage. The emergence of SDN has given another idea to empower the focal programmability of the system. Because of the increasing demand and the scarcity of resources, the load balancing issue needs to be addressed efficiently to manage the incoming traffic and resources and to improve network performance. One of the most critical issues is the role of the controller in SDN to balance the load for having a better Quality of Service (QoS). Though there are few survey articles written on load balancing, there is no detail and systematic review conducted in load balancing in SDN. Hence, this paper extends and reviews the discussion with a taxonomy of current emerging load balancing techniques in SDN systematically by categorizing the techniques as conventional and artificial intelligence-based techniques to improve the service quality. The review also includes the study of metrics and parameters which have been used to measure the performance. This review would allow gaining more information on load balancing approaches in SDN and enables the researchers to fill the current research gaps.

In this work, we tested the performance and scalability on three supercomputers of different architectures including SDSC's Comet, SciNet's GPC and IBM's Blue Gene/Q systems, through benchmarking parallel reservoir simulations. The Comet and GPC systems adopt a fat-tree network and they are connected with InfiniBand interconnects technology. The Blue Gene/Q uses a 5-dimensional toroidal network and it is connected with custom interconnects. In terms of supercomputer architectures, these systems represent two main interconnect families: fat-tree and torus. To demonstrate the application scalability for supercomputers with today's diversified architectures, we benchmark a parallel black oil simulator that is extensively used in the petroleum industry. Our implementation for this simulator is coded in C and MPI, and it offers grids, data, linear solvers, preconditioners, distributed matrices and vectors and modeling modules. Load balancing is based on the Hilbert space-filling curve (HSFC) method. Krylov subspace and AMG solvers are implemented, including restarted GMRES, BiCGSTAB, and AMG solvers from Hypre. The results show that the Comet is the fastest supercomputer among tested systems and the Blue Gene/Q has the best parallel efficiency. The scalability analysis helps to identify the performance barriers for different supercomputer architectures. The study of testing the application performance serves to provide the insights for carrying out parallel reservoir simulations on large-scale computers of different architectures.

This paper reports on the results of a preliminary study in dynamic load balancing on an Intel Hypercube. The purpose of this research is to provide experimental data in how parallel algorithms should be constructed to obtain maximal utilization of a parallel architecture. This study is one aspect of an ongoing research project into the construction of an automated parallelization tool. This tool will take FORTRAN source as input, and construct a parallel algorithm that will produce the same results as the original serial input. The focus of this paper is on the load balancing aspect of that project. The basic idea is to reserve a certain percentage of the computation task, subdivide that percentage into arbitrarily fine tasks, and dole those small tasks out to nodes on request. Ij” the percentage is chosen correctly, then a minority of nodes should be involved in consuming the filler tasks, and the overall throughput of the job should increase as a result of the individual node efJciencies having increased. This paper will outline our approach to performing dynamic load balancing on an Intel iPSC/2. We take the view that the problem of load balancing is really a problem of dividing a “computational task” into smaller components, each of roughly equal complexity, and each an independent event. After this is done, the components of the task can be sent to a node for execution. The key to an optimally balanced load across all computational nodes is the ability to form a statistical profile of the individual components of each computational task. This statistical profile will determine an initial sequence of execution. Our experience indicates that a speedup on the order of 80% is achievable with the judicious use of profiled load balancing. During the process of execution, the initial profile will be altered according to the actual behavior exhibited by the nodes. The difference between the actual and expected performance will be used to determine how much additional time should be devoted to altering the current execution schedule. Currently, our work involves statically setting the load balancing parameters. Our load balancing system determines the execution schedule

Recent work in high-performance computing has shifted attention to PC cluster. For PC-clusters, member nodes are independent computers connected by general-purpose networks. The latency of data communications is long and load balancing among the nodes becomes a critical issue. We introduce a new model for program development on PC clusters, namely the super-programming model (SPM) to address this issue. In SPM, PC clusters are modeled as a single virtual machine with PC as their processing units. The workload is modeled as a collection of super-instructions (SI). Each SI can achieve a limited workload. Application programs are coded using SI. SI is dynamically assigned to available PC at run time. For limited workload, no SI overloads any PC. Therefore, dynamic load balancing becomes an easier task. We apply SPM to mining association rules. Our experiments show that under normal conditions the workload is balanced very well. A performance model is also developed to describe the scalable behavior of SPM.

This paper studies cluster load balancing policies and system support for fine-grain network services. Load balancing on a cluster of machines has been studied extensively in the literature, mainly focusing on coarse-grain distributed computation. Fine-grain services introduce additional challenges because system states fluctuate rapidly,for those services and system performance is highly sensitive to various overhead. The main contribution of our work is to identify effective load balancing schemes for fine-grain services through simulations and empirical evaluations on synthetic workload and real traces. Another contribution is the design and implementation of a load balancing system in a Linux cluster that strikes a balance between acquiring enough load information and minimizing system overhead. Our study concludes that: 1) Random polling based load-balancing policies are well-suited for fine-grain network services: 2) A small poll size provides sufficient information for load balancing, while an excessively large poll size may in fact degrade the performance clue to polling overhead; 3) Discarding slow-responding polls can further improve system performance.

Developing SPMD applications with load balancing

Scalable web servers can be built using a Network of Workstations (NOW) where server capability can be added by adding new workstations as the workload increases. The task of load balancing Hyper Text Transfer Protocol traffic to scalable web servers is the topic of this paper. We present a classification framework for scalable web servers, and present simulations of a clustered web server. The cluster communication is modeled using a detailed, verified model of TCP/IP processing over Asynchronous Transfer Mode. The simulator is a trace driven discrete even simulator, and the traces are obtained from the proxy server of a large Internet Service Provider in Norway. Various load balancing schemes are simulated for Robin load balancing policy implemented in a modified router gives better average response time and better load balancing than the Rotating Nameserver method used in current scalable web servers.

Despite the fast evolution of Distributed Shared Memory (DSM) systems, the load balance problem has not received enough attentions. This problem arises naturally after multithreading was introduced to DSM systems few years ago. The cognizance of it would bring us a significant improvement in system performance. In this paper, we address it by proposing and experimentally evaluating a load balancing method called Dependence-Driven Load Balancing (DDLB) that is dedicated for multithreaded DSM systems. The most attractive feature of this method is to take thread dependence into account in making decisions for migration. In contrast to existing thread scheduling works which for the most part have relied on the information supplied before execution, we require no a priori information. Typically, DDLB embraces three policies, i.e. transfer policy, location policy and selection policy, and applies affinity scheduling. Finally, from the experimental results, the performance of the system with load balancing is improving evidently.

For the cloud computing based on software-defined networks (SDNs), a larger amount of data is collected to cloud for analysis, which will cause the larger amount of redundancy data and longer service response time due to the capacity-limited Internet. To solve this problem, a novel service orchestration and data aggregation framework (SODA) is proposed, which can orchestrate data as services and aggregate data packets to reduce data redundancy and service response delay. In SODA, the network is divided into three layers. 1) Data centers layer (DCL). Data centers (DCs) release software with a specific function to all devices in the network, devices orchestrate data as services and aggregate data packets using software to reduce service response delay. 2) Middle routing layer (MRL). The routing path of data packets in this layer is adjusted according to the correlation of data packets and routing distance. The correlation of data packets is higher and routing distance is short, the probability that data packets are transmitted along the same routing path is higher to reduce redundancy data. 3) Vehicle network layer (VNL). Mobile vehicles are used to transmit data packets and services among devices. A series of experiments and simulation is conducted. The results illustrate that the proposed scheme has better performance compared with the traditional scheme.

Promoting cooperation in selfish computational grids

Adaptive passenger-finding recommendation system for taxi drivers with load balancing problem

The technological development of Internet of Things (IoT) applications is emerging and attracting the attention of the real world in the automated industry, agriculture, environment, and scientific community. In most scenarios, extending the network lifetime of an IoT network is highly challenging because of constrained nodes. The wireless sensor network (WSN) is the core component of IoT applications. In addition, the WSN nodes are required for the network processes, particularly routing, energy maintenance, load balance, congestion control, packet delivery, quick response, and more. The failure of any of the above network processes will affect the entire network operation. IPv6 Routing Protocol for Low-power and Lossy network (RPL) provides high routing solutions to IoT applications requirements. The load balance, congestion control, traffic load, and bottleneck problems are still open issues in the RPL. To resolve the load balance issue, we propose a weighted sum method objective function (WSM-OF), which provides the ability to select the alternative parent in routing by RPL metrics. WSM-OF adopts congestion control and load balancing to avoid heavy traffic and extend the network's node lifetime. The network parameters of control overhead, jitter, packet delivery ratio, parent switching, energy consumption, latency, and network lifetime are implemented and analyzed through the COOJA simulator. The result shows that the WSM-OF improves the network performance and significantly enhances the network lifetime by up to 7.8%.